论文标题
用于复杂灯具的学习辐射场表示
A Learned Radiance-Field Representation for Complex Luminaires
论文作者
论文摘要
我们提出了一种使用高质量的OCTREE发射的代表来渲染复杂照明器的有效方法。复杂的照明器在渲染中是一个特别具有挑战性的问题，因为它们的腐蚀性光路在灯具内部。我们通过使用简单的代理几何形状来降低照明器的几何复杂性，并使用神经辐射场编码视觉复杂的发射光场。我们通过提出专门的损失函数来应对代表灯具的多重挑战，包括其高动态范围，高频含量和空发射区域。为了进行渲染，我们将灯具的nerf提炼成一个普莱诺克特里，我们很容易将其整合到传统的渲染系统中。我们的方法允许在包含最小误差的复杂灯具的场景中加速多达2个数量级。
We propose an efficient method for rendering complex luminaires using a high-quality octree-based representation of the luminaire emission. Complex luminaires are a particularly challenging problem in rendering, due to their caustic light paths inside the luminaire. We reduce the geometric complexity of luminaires by using a simple proxy geometry and encode the visually-complex emitted light field by using a neural radiance field. We tackle the multiple challenges of using NeRFs for representing luminaires, including their high dynamic range, high-frequency content and null-emission areas, by proposing a specialized loss function. For rendering, we distill our luminaires' NeRF into a Plenoctree, which we can be easily integrated into traditional rendering systems. Our approach allows for speed-ups of up to 2 orders of magnitude in scenes containing complex luminaires introducing minimal error.
